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Article Dans Une Revue Peer Community Journal Année : 2023

Structify-Net: Random Graph generation with controlled size and customized structure

Résumé

Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this article, we introduce a framework for the generation of random graphs with a controlled size-number of nodes, edges-and a customizable structure, beyond blocks and spatial ones, based on node-pair rank and a tunable probability function allowing to control the amount of randomness. We introduce a structure zoo-a collection of original network structuresand conduct experiments on the small-world properties of networks generated by those structures. Finally, we introduce an implementation as a Python library named Structifynet.
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Dates et versions

hal-04424059 , version 1 (29-01-2024)

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Rémy Cazabet, Salvatore Citraro, Giulio Rossetti. Structify-Net: Random Graph generation with controlled size and customized structure. Peer Community Journal, 2023, 3, pp.e103. ⟨10.24072/pci.networksci.100114⟩. ⟨hal-04424059⟩
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